In this paper, we propose a new selfreconfiguration scheme for modular robots based on a metamodule design that allows to form a 3D porous structure. The porous structure enables a parallel flow of modules inside it without blocking. The meta-module can also be used to fill its internal volume with an additional number of modules allowing the structure to be compressible and expandable. Hence, it is a potential for improving the self-reconfiguration process. We first present the meta-module model and the porous structure built using it. Then, we describe an algorithm to self-reconfigure the structure from an initial shape to a given goal shape.We evaluated the algorithm in simulation on structures composed of up to 2,700 modules. We studied the performance in term of parallelism, showed that the number of communications is proportional to the number of motions and the execution time varies linearly with the diameter of the configuration.
Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules which are able to coordinate in order to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay and improving the self-configuration processes that focuses on finding a sequence of reconfiguration actions to convert robots from an initial configuration to a goal one. The main idea is to divide the nodes in an initial shape into some clusters based on the final goal shape in order to reduce the time complexity and enhance the self-reconfiguration tasks. In this paper, we propose a robust clustering approach based on a distributed density-cut graph algorithm to divide the networks into a predefined number of clusters based on the final goal shape. The result is an algorithm with linear complexity that scales to large modular robot systems. We implement and demonstrate our algorithm on a real Blinky Blocks system and evaluate it in simulation on networks of up to 30,000 modules.
Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules which are able to coordinate in order to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay and improving the self-reconfiguration process that focuses on finding a sequence of reconfiguration actions to convert robots from an initial shape to a goal one. The main idea of clustering is to divide the modules in an initial shape into a number of groups based on the final goal shape in order to enhance the self-reconfiguration process by allowing clusters to reconfigure in parallel. In this work, we prove that the size-constrained clustering problem is NP-complete and we propose a new tree-based size-constrained clustering algorithm called ”SC-Clust”. The idea is to divide a network into a predefined number of clusters constrained by a given number of modules in each cluster based on the final goal shape. The result is an efficient algorithm that scales to large modular robot systems. To show the efficiency of our approach, we implement and demonstrate our algorithm in simulation on networks of up to 30,000 modules and on the
Blinky Blocks
hardware with up to 144 modules.
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